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Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach

Author

Listed:
  • Lorenzo Boldrini

    (Aarhus University and CREATES)

Abstract

In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based on popular semi-empirical methods present in the literature and on zero-dimensional energy balance models. We show that some of the models developed in the literature on semi-empirical models can be analysed within this framework. We use the sea-level data reconstruction developed in Church and White (2011) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature scenarios of the IPCC fourth assessment report. Furthermore, we propose a bootstrap procedure to compute confidence intervals for the projections, based on the method introduced in Rodriguez and Ruiz (2009).

Suggested Citation

  • Lorenzo Boldrini, 2015. "Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach," CREATES Research Papers 2015-40, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2015-40
    as

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    File URL: https://repec.econ.au.dk/repec/creates/rp/15/rp15_40.pdf
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    References listed on IDEAS

    as
    1. Alejandro Rodriguez & Esther Ruiz, 2009. "Bootstrap prediction intervals in state–space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 167-178, March.
    2. S. J. Koopman & J. Durbin, 2000. "Fast Filtering and Smoothing for Multivariate State Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(3), pages 281-296, May.
    3. David Anthoff & Robert Nicholls & Richard Tol, 2010. "The economic impact of substantial sea-level rise," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(4), pages 321-335, April.
    4. Bergstrom, A.R., 1997. "Gaussian Estimation of Mixed-Order Continuous-Time Dynamic Models with Unobservable Stochastic Trends from Mixed Stock and Flow Data," Econometric Theory, Cambridge University Press, vol. 13(4), pages 467-505, February.
    5. Felix Pretis, 2015. "Econometric Models of Climate Systems: The Equivalence of Two-Component Energy Balance Models and Cointegrated VARs," Economics Series Working Papers 750, University of Oxford, Department of Economics.
    6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    energy balance model; semi-empirical model; state-space system; Kalman filter; forecasting; temperature; sea level; bootstrap JEL classification: C32;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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